Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 400922
Title Analysis of monotonic greening and browning trends from global NDVI time-series
Author(s) Jong, R. de; Bruin, S. de; Wit, A.J.W. de; Schaepman, M.E.; Dent, D.L.
Source Remote Sensing of Environment 115 (2011)2. - ISSN 0034-4257 - p. 692 - 702.
DOI http://dx.doi.org/10.1016/j.rse.2010.10.011
Department(s) Laboratory of Geo-information Science and Remote Sensing
CGI - Earth Observation
PE&RC
ISRIC - World Soil Information
Publication type Refereed Article in a scientific journal
Publication year 2011
Keyword(s) avhrr vegetation index - land degradation - spot-vegetation - growing-season - photosynthetic trends - primary productivity - deciduous forest - plant phenology - carbon-dioxide - high-latitudes
Abstract Remotely sensed vegetation indices are widely used to detect greening and browning trends; especially the global coverage of time-series normalized difference vegetation index (NDVI) data which are available from 1981. Seasonality and serial auto-correlation in the data have previously been dealt with by integrating the data to annual values; as an alternative to reducing the temporal resolution, we apply harmonic analyses and non-parametric trend tests to the GIMMS NDVI dataset (1981–2006). Using the complete dataset, greening and browning trends were analyzed using a linear model corrected for seasonality by subtracting the seasonal component, and a seasonal non-parametric model. In a third approach, phenological shift and variation in length of growing season were accounted for by analyzing the time-series using vegetation development stages rather than calendar days. Results differed substantially between the models, even though the input data were the same. Prominent regional greening trends identified by several other studies were confirmed but the models were inconsistent in areas with weak trends. The linear model using data corrected for seasonality showed similar trend slopes to those described in previous work using linear models on yearly mean values. The non-parametric models demonstrated the significant influence of variations in phenology; accounting for these variations should yield more robust trend analyses and better understanding of vegetation trends.
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